In the learning algorithms, Bias are generally considered as errors that declare their presence due to OVERLY assumptions. These can sometimes result in failure of entire model and can largely affect the accuracy ALSO in several CASES. Some experts believe these errors are essential to enable learners gain knowledge from a training point of view. On the other side, Variance is another problem that comes when the learning algorithm is quite COMPLEX. Therefore a LIMIT is to be imposed on this.

In the learning algorithms, Bias are generally considered as errors that declare their presence due to overly assumptions. These can sometimes result in failure of entire model and can largely affect the accuracy also in several cases. Some experts believe these errors are essential to enable learners gain knowledge from a training point of view. On the other side, Variance is another problem that comes when the learning algorithm is quite complex. Therefore a limit is to be imposed on this.